Intelligent air cleaner, indoor air quality control method and control apparatus using intelligent air cleaner

ABSTRACT

An indoor air quality control method using an intelligent air cleaner is disclosed. An indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention can predict indoor dust concentration progress on the basis of output values of a learning model having received dust concentration data as input values when the dust concentration data of an indoor place where the air cleaner is located is received from the air cleaner, and determine whether ventilation is required by comparing the predicted progress with outside dust concentration data received from the Meteorological Administration server. Accordingly, indoor dust concentration changes can be predicted and an appropriate ventilation time can be recommended. The intelligent air cleaner and the indoor air quality control method using the same of the present invention can be associated with artificial intelligence modules, devices related with the 5G service, and the like.

TECHNICAL FIELD

The present invention relates to an intelligent air cleaner, an indoor air quality control method and control apparatus using the intelligent air cleaner, and more specifically, to an intelligent air cleaner capable of recommending an appropriate ventilation time by predicting indoor dust concentration progress, and an indoor air quality control method and control apparatus using the intelligent air cleaner.

BACKGROUND ART

An air cleaner has a function of eliminating fine dusts or harmful substances in the air and purifying the air. Such an air cleaner is required to minimize energy consumption and effectively control pollutants in the air.

Meanwhile, in cases in which a degree of pollution is very high, such as cooking and cleaning for a long time in indoor spaces, ventilation may be a more efficient method rather than operating an air cleaner indoors.

Furthermore, considering that the lifespan of a filter may be considerably affected when an air cleaner is continuously driven in order to eliminate indoor fine dusts, it is necessary to actively reflect a state of outside air quality in air purification.

DISCLOSURE Technical Problem

An object of the present invention is to solve the aforementioned necessity and/or problems.

Further, the present invention provides an indoor air quality control method using an intelligent air cleaner which can recommend an appropriate ventilation time by predicting indoor dust concentration progress.

Further, the present invention provides an indoor air quality control method using an intelligent air cleaner which can recommend an appropriate ventilation time by comparing a predicted indoor dust concentration with a degree of outside air pollution.

Further, the present invention provides an indoor air quality control method using an intelligent air cleaner which can minimize the amount of input data using a deep learning model and efficiently manage the lifespan of a filter of the air cleaner by predicting indoor dust concentration progress on the basis of the minimized amount of input data and recommending ventilation instead of air purification using the air cleaner.

Further, the present invention provides an indoor air quality control method using an intelligent air cleaner which can efficiently manage the air cleaner by comparing indoor dust concentration progress with a real-time outside fine dust concentration and providing a ventilation alarm to the air cleaner or a user terminal.

Technical Solution

An indoor air quality control method using an intelligent air cleaner according to one aspect of the present invention includes: receiving, from the air cleaner, dust concentration data of an indoor place where the air cleaner is located; predicting indoor dust concentration progress on the basis of output values of a learning model having the received dust concentration data as input values; receiving outside dust concentration data from an external server; determining whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data; and controlling an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed at predetermined intervals.

The receiving of the indoor dust concentration data from the air cleaner may include receiving a certain percentage or higher of data in a bad state on the basis of PM 2.5 forecast from among data sensed by the air cleaner at predetermined intervals.

The predicting of the indoor dust concentration progress may include: determining whether dust concentration data continuously sensed N times exceeds the predetermined reference value; and defining data of N dust concentrations as input values of a deep learning model and predicting the indoor dust concentration progress through output values of the deep learning model.

The indoor air quality control method may further include, when progress of additional N dust concentrations sensed after the N dust concentrations are sensed is predicted to increase as a result of prediction of the indoor dust concentration progress: requesting the outside dust concentration data from the Meteorological Administration server; and determining that ventilation is required when an average value of input data of the deep learning model is greater than the outside dust concentration data received from the Meteorological Administration server.

The indoor dust concentration progress may be predicted as one of a pattern in which the same number of dust concentrations as the number of pieces of dust concentration data received from the intelligent air cleaner increase, a pattern in which the dust concentrations decrease and a pattern in which the dust concentrations remains in a current state.

The determining of whether ventilation is required may include determining that ventilation is required when the indoor dust concentration progress is determined to be the increasing pattern or the current state remaining pattern.

The determining of whether ventilation is required may include: determining that ventilation is not required when the indoor dust concentration progress is predicted as the increasing pattern and an outside dust concentration received from the external server is higher than the indoor dust concentration; and controlling the air cleaner to continuously operate.

The determining of whether ventilation is required may further include predicting a ventilation time, wherein it is determined that ventilation is required when the indoor dust concentration progress is predicted as the decreasing pattern and it is predicted that the outside dust concentration is lower than the indoor dust concentration at a specific time of the decreasing pattern.

The controlling of output of the alarm includes controlling an indoor dust concentration state and whether ventilation is required to be output through audio.

The receiving of the indoor dust concentration data may further include receiving, in a state in which the air cleaner is powered off, the indoor dust concentration data sensed through a sensing unit in a state in which the air cleaner is in a standby state, and the indoor air quality control method may further include controlling the air controller to be activated when it is determined that ventilation is required.

The determining of whether ventilation is required may include adaptively controlling a ventilation recommendation standard in consideration of characteristics of an occupant residing in the indoor place.

An indoor air quality control apparatus using an intelligent air cleaner according to another aspect of the present invention includes: an RF communication unit a storage unit storing a deep learning model; and a processor configured to determine whether to perform ventilation in a space in which the air cleaner is located on the basis of indoor dust concentration data received from the air cleaner and outside dust concentration data received from the Meteorological Administration server, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed at predetermined intervals, and the processor predicts indoor dust concentration progress on the basis of output values of the deep learning model having the received dust concentration data as input values and controls an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required.

An indoor air quality control system according to another aspect of the present invention includes: an intelligence air cleaner for acquiring indoor dust concentration data; and a cloud server for receiving the indoor dust concentration data from the air cleaner, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed by the air cleaner at predetermined intervals, and the cloud server predicts indoor dust concentration progress on the basis of output values of a deep learning model having the received dust concentration data as input values, receives outside dust concentration data from an external server, determines whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data, and outputs an alarm to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required.

A non-transitory computer-readable medium according to another aspect of the present invention stores a computer-executable component configured to be executed in one or more processors of a computer device, wherein the computer-executable component is configured: to receive, from an air cleaner, dust concentration data of an indoor place where the air cleaner is located; to predict indoor dust concentration progress on the basis of output values of a learning model having the received dust concentration data as input values; to receive outside dust concentration data from an external server; to determine whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data; and to control an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required, wherein the received dust concentration data includes data exceeding a predetermined reference value among data sensed at predetermined intervals.

Advantageous Effects

The effects of the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention will be described below.

According to the present invention, it is possible to recommend an appropriate ventilation time by predicting indoor dust concentration progress.

Further, according to the present invention, it is possible to recommend an appropriate ventilation time by comparing a predicted indoor dust concentration with a degree of outside air pollution.

Further, according to the present invention, it is possible to minimize the amount of input data using a deep learning model and efficiently manage the lifespan of a filter of the air cleaner by predicting indoor dust concentration progress on the basis of the minimized amount of input data and recommending ventilation instead of air purification using the air cleaner.

Further, according to the present invention, it is possible to efficiently manage the air cleaner by comparing indoor dust concentration progress with a real-time outside fine dust concentration and providing a ventilation alarm to the air cleaner or a user terminal.

It will be appreciated by persons skilled in the art that the effects that could be achieved with the present invention are not limited to what has been particularly described hereinabove and the above and other effects that the present invention could achieve will be more clearly understood from the following detailed description.

DESCRIPTION OF DRAWINGS

Accompanying drawings included as a part of the detailed description for helping understand the present invention provide embodiments of the present invention and are provided to describe technical features of the present invention with the detailed description.

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

FIG. 2 shows an example of a signal transmission/reception method in a wireless communication system.

FIG. 3 shows an example of basic operations of an user equipment and a 5G network in a 5G communication system.

FIG. 4 schematically illustrates a system for implementing an indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention.

FIG. 5 is a block diagram of an AI apparatus applicable to embodiments of the present invention.

FIG. 6 is an exemplary block diagram of an indoor air quality control apparatus using an intelligent air cleaner according to an embodiment of the present invention.

FIG. 7 illustrates a data flow for implementing the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention.

FIG. 8 is a flowchart of the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention.

FIG. 9 is a diagram for describing a detailed operation of a cloud server for performing the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention.

FIG. 10 is a diagram for describing deep learning model learning standards according to an embodiment of the present invention.

FIG. 11 is a diagram for describing various examples of indoor air quality patterns used for deep learning model learning through data clustering according to an embodiment of the present invention.

FIG. 12 is a diagram for describing deep learning model learning patterns through input factors and result factors according to an embodiment of the present invention.

FIG. 13 is a diagram for describing an example of determining necessity for ventilation using a learned deep learning model according to an embodiment of the present invention.

FIGS. 14 and 15 are diagrams for describing results of tests to which the indoor air quality control method using an intelligent air cleaner according to the present invention is applied.

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.

MODE FOR INVENTION

Hereinafter, embodiments of the disclosure will be described in detail with reference to the attached drawings. The same or similar components are given the same reference numbers and redundant description thereof is omitted. The suffixes “module” and “unit” of elements herein are used for convenience of description and thus can be used interchangeably and do not have any distinguishable meanings or functions. Further, in the following description, if a detailed description of known techniques associated with the present invention would unnecessarily obscure the gist of the present invention, detailed description thereof will be omitted. In addition, the attached drawings are provided for easy understanding of embodiments of the disclosure and do not limit technical spirits of the disclosure, and the embodiments should be construed as including all modifications, equivalents, and alternatives falling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describe various components, such components must not be limited by the above terms. The above terms are used only to distinguish one component from another.

When an element is “coupled” or “connected” to another element, it should be understood that a third element may be present between the two elements although the element may be directly coupled or connected to the other element. When an element is “directly coupled” or “directly connected” to another element, it should be understood that no element is present between the two elements.

The singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood that the terms “comprise” and “include” specify the presence of stated features, integers, steps, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or combinations.

Hereinafter, 5G communication (5th generation mobile communication) required by an apparatus requiring AI processed information and/or an AI processor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 1 is a block diagram of a wireless communication system to which methods proposed in the disclosure are applicable.

Referring to FIG. 1, a device (autonomous device) including an autonomous module is defined as a first communication device (910 of FIG. 1), and a processor 911 can perform detailed autonomous operations.

A 5G network including another vehicle communicating with the autonomous device is defined as a second communication device (920 of FIG. 1), and a processor 921 can perform detailed autonomous operations.

The 5G network may be represented as the first communication device and the autonomous device may be represented as the second communication device.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, an autonomous device, or the like.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, a vehicle, a vehicle having an autonomous function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a Fin Tech device (or financial device), a security device, a climate/environment device, a device associated with 5G services, or other devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, personal digital assistants (PDAs), a portable multimedia player (PMP), a navigation device, a slate PC, a tablet PC, an ultrabook, a wearable device (e.g., a smartwatch, a smart glass and a head mounted display (HMD)), etc. For example, the HMD may be a display device worn on the head of a user. For example, the HMD may be used to realize VR, AR or MR. For example, the drone may be a flying object that flies by wireless control signals without a person therein. For example, the VR device may include a device that implements objects or backgrounds of a virtual world. For example, the AR device may include a device that connects and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the MR device may include a device that unites and implements objects or background of a virtual world to objects, backgrounds, or the like of a real world. For example, the hologram device may include a device that implements 360-degree 3D images by recording and playing 3D information using the interference phenomenon of light that is generated by two lasers meeting each other which is called holography. For example, the public safety device may include an image repeater or an imaging device that can be worn on the body of a user. For example, the MTC device and the IoT device may be devices that do not require direct interference or operation by a person. For example, the MTC device and the IoT device may include a smart meter, a bending machine, a thermometer, a smart bulb, a door lock, various sensors, or the like. For example, the medical device may be a device that is used to diagnose, treat, attenuate, remove, or prevent diseases. For example, the medical device may be a device that is used to diagnose, treat, attenuate, or correct injuries or disorders. For example, the medial device may be a device that is used to examine, replace, or change structures or functions. For example, the medical device may be a device that is used to control pregnancy. For example, the medical device may include a device for medical treatment, a device for operations, a device for (external) diagnose, a hearing aid, an operation device, or the like. For example, the security device may be a device that is installed to prevent a danger that is likely to occur and to keep safety. For example, the security device may be a camera, a CCTV, a recorder, a black box, or the like. For example, the Fin Tech device may be a device that can provide financial services such as mobile payment.

Referring to FIG. 1, the first communication device 910 and the second communication device 920 include processors 911 and 921, memories 914 and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rx module 915 transmits a signal through each antenna 926. The processor implements the aforementioned functions, processes and/or methods. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium. More specifically, the Tx processor 912 implements various signal processing functions with respect to L1 (i.e., physical layer) in DL (communication from the first communication device to the second communication device). The Rx processor implements various signal processing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the first communication device) is processed in the first communication device 910 in a way similar to that described in association with a receiver function in the second communication device 920. Each Tx/Rx module 925 receives a signal through each antenna 926. Each Tx/Rx module provides RF carriers and information to the Rx processor 923. The processor 921 may be related to the memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 2 is a diagram showing an example of a signal transmission/reception method in a wireless communication system.

Referring to FIG. 2, when a UE is powered on or enters a new cell, the UE performs an initial cell search operation such as synchronization with a BS (S201). For this operation, the UE can receive a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS to synchronize with the BS and acquire information such as a cell ID. In LTE and NR systems, the P-SCH and S-SCH are respectively called a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). After initial cell search, the UE can acquire broadcast information in the cell by receiving a physical broadcast channel (PBCH) from the BS. Further, the UE can receive a downlink reference signal (DL RS) in the initial cell search step to check a downlink channel state. After initial cell search, the UE can acquire more detailed system information by receiving a physical downlink shared channel (PDSCH) according to a physical downlink control channel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radio resource for signal transmission, the UE can perform a random access procedure (RACH) for the BS (steps S203 to S206). To this end, the UE can transmit a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205) and receive a random access response (RAR) message for the preamble through a PDCCH and a corresponding PDSCH (S204 and S206). In the case of a contention-based RACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can perform PDCCH/PDSCH reception (S207) and physical uplink shared channel (PUSCH)/physical uplink control channel (PUCCH) transmission (S208) as normal uplink/downlink signal transmission processes. Particularly, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors a set of PDCCH candidates in monitoring occasions set for one or more control element sets (CORESET) on a serving cell according to corresponding search space configurations. A set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, and a search space set may be a common search space set or a UE-specific search space set. CORESET includes a set of (physical) resource blocks having a duration of one to three OFDM symbols. A network can configure the UE such that the UE has a plurality of CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting decoding of PDCCH candidate(s) in a search space. When the UE has successfully decoded one of PDCCH candidates in a search space, the UE determines that a PDCCH has been detected from the PDCCH candidate and performs PDSCH reception or PUSCH transmission on the basis of DCI in the detected PDCCH. The PDCCH can be used to schedule DL transmissions over a PDSCH and UL transmissions over a PUSCH. Here, the DCI in the PDCCH includes downlink assignment (i.e., downlink grant (DL grant)) related to a physical downlink shared channel and including at least a modulation and coding format and resource allocation information, or an uplink grant (UL grant) related to a physical uplink shared channel and including a modulation and coding format and resource allocation information.

An initial access (IA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

The UE can perform cell search, system information acquisition, beam alignment for initial access, and DL measurement on the basis of an SSB. The SSB is interchangeably used with a synchronization signal/physical broadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in four consecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH is transmitted for each OFDM symbol. Each of the PSS and the SSS includes one OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDM symbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequency synchronization of a cell and detects a cell identifier (ID) (e.g., physical layer cell ID (PCI)) of the cell. The PSS is used to detect a cell ID in a cell ID group and the SSS is used to detect a cell ID group. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group. A total of 1008 cell IDs are present. Information on a cell ID group to which a cell ID of a cell belongs is provided/acquired through an SSS of the cell, and information on the cell ID among 336 cell ID groups is provided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity. A default SSB periodicity assumed by a UE during initial cell search is defined as 20 ms. After cell access, the SSB periodicity can be set to one of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., a BS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIBs). SI other than the MIB may be referred to as remaining minimum system information. The MIB includes information/parameter for monitoring a PDCCH that schedules a PDSCH carrying SIB1 (SystemInformationBlock1) and is transmitted by a BS through a PBCH of an SSB. SIB1 includes information related to availability and scheduling (e.g., transmission periodicity and SI-window size) of the remaining SIBs (hereinafter, SIBx, x is an integer equal to or greater than 2). SiBx is included in an SI message and transmitted over a PDSCH. Each SI message is transmitted within a periodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will be additionally described with reference to FIG. 2.

A random access procedure is used for various purposes. For example, the random access procedure can be used for network initial access, handover, and UE-triggered UL data transmission. A UE can acquire UL synchronization and UL transmission resources through the random access procedure. The random access procedure is classified into a contention-based random access procedure and a contention-free random access procedure. A detailed procedure for the contention-based random access procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of a random access procedure in UL. Random access preamble sequences having different two lengths are supported. A long sequence length 839 is applied to subcarrier spacings of 1.25 kHz and 5 kHz and a short sequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz, 60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BS transmits a random access response (RAR) message (Msg2) to the UE. A PDCCH that schedules a PDSCH carrying a RAR is CRC masked by a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI) and transmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UE can receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH. The UE checks whether the RAR includes random access response information with respect to the preamble transmitted by the UE, that is, Msg1. Presence or absence of random access information with respect to Msg1 transmitted by the UE can be determined according to presence or absence of a random access preamble ID with respect to the preamble transmitted by the UE. If there is no response to Msg1, the UE can retransmit the RACH preamble less than a predetermined number of times while performing power ramping. The UE calculates PRACH transmission power for preamble retransmission on the basis of most recent pathloss and a power ramping counter.

The UE can perform UL transmission through Msg3 of the random access procedure over a physical uplink shared channel on the basis of the random access response information. Msg3 can include an RRC connection request and a UE ID. The network can transmit Msg4 as a response to Msg3, and Msg4 can be handled as a contention resolution message on DL. The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB or a CSI-RS and (2) a UL BM procedure using a sounding reference signal (SRS). In addition, each BM procedure can include Tx beam swiping for determining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channel state information (CSI)/beam is configured in RRC_CONNECTED.

-   -   A UE receives a CSI-ResourceConfig IE including         CSI-SSB-ResourceSetList for SSB resources used for BM from a BS.         The RRC parameter “csi-SSB-ResourceSetList” represents a list of         SSB resources used for beam management and report in one         resource set. Here, an SSB resource set can be set as {SSBx1,         SSBx2, SSBx3, SSBx4, . . . }. An SSB index can be defined in the         range of 0 to 63.     -   The UE receives the signals on SSB resources from the BS on the         basis of the CSI-SSB-ResourceSetList.     -   When CSI-RS reportConfig with respect to a report on SSBRI and         reference signal received power (RSRP) is set, the UE reports         the best SSBRI and RSRP corresponding thereto to the BS. For         example, when reportQuantity of the CSI-RS reportConfig IE is         set to ‘ssb-Index-RSRP’, the UE reports the best SSBRI and RSRP         corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSB and ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and the SSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here, QCL-TypeD may mean that antenna ports are quasi co-located from the viewpoint of a spatial Rx parameter. When the UE receives signals of a plurality of DL antenna ports in a QCL-TypeD relationship, the same Rx beam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beam swiping procedure of a BS using a CSI-RS will be sequentially described. A repetition parameter is set to ‘ON’ in the Rx beam determination procedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of a BS.

First, the Rx beam determination procedure of a UE will be described.

-   -   The UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from a BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is set to ‘ON’.     -   The UE repeatedly receives signals on resources in a CSI-RS         resource set in which the RRC parameter ‘repetition’ is set to         ‘ON’ in different OFDM symbols through the same Tx beam (or DL         spatial domain transmission filters) of the BS.     -   The UE determines an RX beam thereof.     -   The UE skips a CSI report. That is, the UE can skip a CSI report         when the RRC parameter ‘repetition’ is set to ‘ON’.

Next, the Tx beam determination procedure of a BS will be described.

-   -   A UE receives an NZP CSI-RS resource set IE including an RRC         parameter with respect to ‘repetition’ from the BS through RRC         signaling. Here, the RRC parameter ‘repetition’ is related to         the Tx beam swiping procedure of the BS when set to ‘OFF’.     -   The UE receives signals on resources in a CSI-RS resource set in         which the RRC parameter ‘repetition’ is set to ‘OFF’ in         different DL spatial domain transmission filters of the BS.     -   The UE selects (or determines) a best beam.     -   The UE reports an ID (e.g., CRI) of the selected beam and         related quality information (e.g., RSRP) to the BS. That is,         when a CSI-RS is transmitted for BM, the UE reports a CRI and         RSRP with respect thereto to the BS.

Next, the UL BM procedure using an SRS will be described.

-   -   A UE receives RRC signaling (e.g., SRS-Config IE) including a         (RRC parameter) purpose parameter set to ‘beam management” from         a BS. The SRS-Config IE is used to set SRS transmission. The         SRS-Config IE includes a list of SRS-Resources and a list of         SRS-ResourceSets. Each SRS resource set refers to a set of         SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted on the basis of SRS-SpatialRelation Info included in the SRS-Config IE. Here, SRS-SpatialRelation Info is set for each SRS resource and indicates whether the same beamforming as that used for an SSB, a CSI-RS or an SRS will be applied for each SRS resource.

-   -   When SRS-SpatialRelationInfo is set for SRS resources, the same         beamforming as that used for the SSB, CSI-RS or SRS is applied.         However, when SRS-SpatialRelationInfo is not set for SRS         resources, the UE arbitrarily determines Tx beamforming and         transmits an SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occur due to rotation, movement or beamforming blockage of a UE. Accordingly, NR supports BFR in order to prevent frequent occurrence of RLF. BFR is similar to a radio link failure recovery procedure and can be supported when a UE knows new candidate beams. For beam failure detection, a BS configures beam failure detection reference signals for a UE, and the UE declares beam failure when the number of beam failure indications from the physical layer of the UE reaches a threshold set through RRC signaling within a period set through RRC signaling of the BS. After beam failure detection, the UE triggers beam failure recovery by initiating a random access procedure in a PCell and performs beam failure recovery by selecting a suitable beam. (When the BS provides dedicated random access resources for certain beams, these are prioritized by the UE). Completion of the aforementioned random access procedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively low traffic size, (2) a relatively low arrival rate, (3) extremely low latency requirements (e.g., 0.5 and 1 ms), (4) relatively short transmission duration (e.g., 2 OFDM symbols), (5) urgent services/messages, etc. In the case of UL, transmission of traffic of a specific type (e.g., URLLC) needs to be multiplexed with another transmission (e.g., eMBB) scheduled in advance in order to satisfy more stringent latency requirements. In this regard, a method of providing information indicating preemption of specific resources to a UE scheduled in advance and allowing a URLLC UE to use the resources for UL transmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB and URLLC services can be scheduled on non-overlapping time/frequency resources, and URLLC transmission can occur in resources scheduled for ongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCH transmission of the corresponding UE has been partially punctured and the UE may not decode a PDSCH due to corrupted coded bits. In view of this, NR provides a preemption indication. The preemption indication may also be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receives DownlinkPreemption IE through RRC signaling from a BS. When the UE is provided with DownlinkPreemption IE, the UE is configured with INT-RNTI provided by a parameter int-RNTI in DownlinkPreemption IE for monitoring of a PDCCH that conveys DCI format 2_1. The UE is additionally configured with a corresponding set of positions for fields in DCI format 2_1 according to a set of serving cells and positionInDCI by INT-ConfigurationPerServing Cell including a set of serving cell indexes provided by servingCellID, configured having an information payload size for DCI format 2_1 according to dci-Payloadsize, and configured with indication granularity of time-frequency resources according to timeFrequency Sect.

The UE receives DCI format 2_1 from the BS on the basis of the DownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configured set of serving cells, the UE can assume that there is no transmission to the UE in PRBs and symbols indicated by the DCI format 2_1 in a set of PRBs and a set of symbols in a last monitoring period before a monitoring period to which the DCI format 2_1 belongs. For example, the UE assumes that a signal in a time-frequency resource indicated according to preemption is not DL transmission scheduled therefor and decodes data on the basis of signals received in the remaining resource region.

E. mMTC (Massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios for supporting a hyper-connection service providing simultaneous communication with a large number of UEs. In this environment, a UE intermittently performs communication with a very low speed and mobility. Accordingly, a main goal of mMTC is operating a UE for a long time at a low cost. With respect to mMTC, 3GPP deals with MTC and NB (NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, a PDSCH (physical downlink shared channel), a PUSCH, etc., frequency hopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH) including specific information and a PDSCH (or a PDCCH) including a response to the specific information are repeatedly transmitted. Repetitive transmission is performed through frequency hopping, and for repetitive transmission, (RF) retuning from a first frequency resource to a second frequency resource is performed in a guard period and the specific information and the response to the specific information can be transmitted/received through a narrowband (e.g., 6 resource blocks (RBs) or 1 RB).

F. Basic Operation Between Autonomous Vehicles Using 5G Communication

FIG. 3 shows an example of basic operations of an autonomous vehicle and a 5G network in a 5G communication system.

The autonomous vehicle transmits specific information to the 5G network (S1). The specific information may include autonomous driving related information. In addition, the 5G network can determine whether to remotely control the vehicle (S2). Here, the 5G network may include a server or a module which performs remote control related to autonomous driving. In addition, the 5G network can transmit information (or signal) related to remote control to the autonomous vehicle (S3).

G. Applied Operations Between Autonomous Vehicle and 5G Network in 5G Communication System

Hereinafter, the operation of an autonomous vehicle using 5G communication will be described in more detail with reference to wireless communication technology (BM procedure, URLLC, mMTC, etc.) described in FIGS. 1 and 2.

First, a basic procedure of an applied operation to which a method proposed by the present invention which will be described later and eMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 3, the autonomous vehicle performs an initial access procedure and a random access procedure with the 5G network prior to step S1 of FIG. 3 in order to transmit/receive signals, information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial access procedure with the 5G network on the basis of an SSB in order to acquire DL synchronization and system information. A beam management (BM) procedure and a beam failure recovery procedure may be added in the initial access procedure, and quasi-co-location (QCL) relation may be added in a process in which the autonomous vehicle receives a signal from the 5G network.

In addition, the autonomous vehicle performs a random access procedure with the 5G network for UL synchronization acquisition and/or UL transmission. The 5G network can transmit, to the autonomous vehicle, a UL grant for scheduling transmission of specific information. Accordingly, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. In addition, the 5G network transmits, to the autonomous vehicle, a DL grant for scheduling transmission of 5G processing results with respect to the specific information. Accordingly, the 5G network can transmit, to the autonomous vehicle, information (or a signal) related to remote control on the basis of the DL grant.

Next, a basic procedure of an applied operation to which a method proposed by the present invention which will be described later and URLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemption IE from the 5G network after the autonomous vehicle performs an initial access procedure and/or a random access procedure with the 5G network. Then, the autonomous vehicle receives DCI format 2_1 including a preemption indication from the 5G network on the basis of DownlinkPreemption IE. The autonomous vehicle does not perform (or expect or assume) reception of eMBB data in resources (PRBs and/or OFDM symbols) indicated by the preemption indication. Thereafter, when the autonomous vehicle needs to transmit specific information, the autonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a method proposed by the present invention which will be described later and mMTC of 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 3 which are changed according to application of mMTC.

In step S1 of FIG. 3, the autonomous vehicle receives a UL grant from the 5G network in order to transmit specific information to the 5G network. Here, the UL grant may include information on the number of repetitions of transmission of the specific information and the specific information may be repeatedly transmitted on the basis of the information on the number of repetitions. That is, the autonomous vehicle transmits the specific information to the 5G network on the basis of the UL grant. Repetitive transmission of the specific information may be performed through frequency hopping, the first transmission of the specific information may be performed in a first frequency resource, and the second transmission of the specific information may be performed in a second frequency resource. The specific information can be transmitted through a narrowband of 6 resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined with methods proposed in the present invention which will be described later and applied or can complement the methods proposed in the present invention to make technical features of the methods concrete and clear.

FIG. 4 schematically illustrates a system in which an indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention is implemented.

Referring to FIG. 4, a system in which the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention is implemented may include an intelligent air cleaner 10 and a cloud server 20.

The intelligent air cleaner 10 can transmit dust concentration data sensed in the intelligent air cleaner 10 to the cloud server 20 by performing data communication with the cloud server 20.

The cloud server 20 can perform AI processing on the basis of various types of indoor dust concentration data collected from the intelligent air cleaner 10. The cloud server 20 includes an AI system, an AI module and an AI apparatus for performing AI processing and each of the AI system, the AI module and the AI apparatus may include at least one learning model. The cloud server 20 can transmit AI processing results with respect to dust concentration data received from the intelligent air cleaner 10 to the intelligent air cleaner 10 or transmit a control signal of the intelligent air cleaner 10 according to AI processing results.

Here, the cloud server 20 predicts a dust concentration pattern for each time period which is collected from the intelligent air cleaner 10 and transmits predicted results to the intelligent air cleaner 10. The intelligent air cleaner 10 can control the air cleaning intensity thereof on the basis of the predicted results.

According to an embodiment of the present invention, the cloud server 20 can predict a dust concentration pattern for each time period and cause the air cleaner 10 to adjust the air cleaning intensity thereof to a predetermined intensity before arrival of a time interval predicted as an interval in which an indoor dust concentration will be high in a state in which the air cleaner 10 is aware of the predicted time interval, to thereby manage air quality more efficiently in the predicted time interval.

FIG. 5 is a block diagram of an AI apparatus applicable to embodiments of the present invention.

Referring to FIG. 5, an AI apparatus 20 may include an electronic device including an AI module which can perform AI processing, a server including the AI module, or the like. Further, the AI apparatus 20 may be included as at least a component of an air cleaner to perform at least part of AI processing.

AI processing can include all operations related to a controller 140 of an air cleaner. For example, the air cleaner can execute AI processing of air cleanliness or humidity information to perform processing/determination, control signal generation operations.

FIG. 7 illustrates a data flow for implementing an indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention.

Referring to FIG. 7, the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention can be implemented through data communication with the intelligent air cleaner 10, a 5G network and a Korea Meteorological Administration (KMA) server. Here, the 5G network may include the cloud server 20 in the present invention. The 5G network is called the cloud server 20 for convenience of description. Further, the air cleaner 10 may refer to the intelligent air cleaner 10 in this specification.

Referring to FIG. 7, the cloud server 20 can transmit a control signal for setting the intelligent air cleaner 10 to an artificial intelligence mode to the air cleaner 10 (S700).

When the air cleaner 10 operates in a normal mode distinguished from the artificial intelligence mode, a ventilation recommendation service provided in an embodiment of the present invention may not be provided. However, only an alarm is provided while the ventilation recommendation service is provided even in the normal mode, and the ventilation recommendation service can be output through an output unit when the alarm is provided and operation of the air cleaner 10 can be continuously performed in the normal mode. However, when the ventilation recommendation service is provided in the artificial intelligence mode, operation of the air cleaner is automatically terminated and an operation of controlling a window to be opened through a window system connected to a home network service can be performed.

When the artificial intelligence mode is set, the intelligent air cleaner 10 can sense dust concentration data through a sensing unit (S710).

In the artificial intelligence mode, the intelligent air cleaner 10 can determine whether dust concentration data sensed for a predetermined time exceeds a reference value (S720). For example, the intelligence air cleaner 10 can store the average of PM 2.5 data for five minutes. When six or more pieces of data are generated on the basis of low air quality, dust data can be transmitted to the cloud server 20 (S730).

The air cleaner 10 can transmit dust concentration data to the cloud server 20 whenever a PM 2.5 fine dust concentration is generated six times on the basis of low air quality according to the aforementioned standard. Here, PM-2.5 (Particulate Matter Less than 2.5 μm) refers to dust with a particle size of 2.5 μm or less. This is referred to as a fine particulate matter. According to results that a smaller particulate size greatly affects health, advanced countries have started introduction of criteria for particulate matters from the late 90s. Korea announced criteria of an annual average of 25 μg/m³ and an average of 35 μg/m³ for twenty-four hours, and the US set criteria of an annual average of 15 μg/m³ and an average of 35 μg/m³ for twenty-four hours. This is called fine particulate matters.

Meanwhile, the cloud server 20 can determine whether ventilation is required with reference to a degree of outside air pollution of a point where the air cleaner 10 is positioned on the basis of dust concentration data collected from the air cleaner 10. To this end, the cloud server 20 can receive outside dust concentrations from the KMA server (30 in FIG. 4) (S740).

The cloud server 20 can compare a degree of indoor air pollution collected from the air cleaner 10 with KMA server data to determine necessity for ventilation (S750).

Further, the cloud server 20 can transmit a ventilation recommendation alarm message to the air cleaner 10. The air cleaner 10 can output the received ventilation recommendation alarm message to a display unit or through an audio output unit. Further, the cloud server 20 may transmit the recommendation alarm message to a user terminal.

FIG. 8 is a flowchart of the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention. FIG. 8 illustrates an operation in the cloud server 20 and the operation can be implemented through a processor of the cloud server 20.

Referring to FIG. 8, the processor of the cloud server 20 can receive indoor dust concentration data collected by the air cleaner (S800).

Here, the received dust concentration data may be data that exceeds a predetermined reference value among data sensed at predetermined intervals. As described above, the air cleaner 10 stores sensed dust concentration data and can store an average of PM 2.5 data. Further, the air cleaner 10 can transmit indoor dust concentration data to the cloud server 20 when six or more reference values for high indoor fine dust concentrations are generated on the basis of PM 2.5 data.

The processor can predict indoor dust concentration progress by applying dust concentration data received from the air cleaner to a learning model (S810).

The processor can apply the received dust concentration data as input values of a deep learning model stored in a memory of the cloud server 20. Indoor air quality progress after a time when dust concentration data received from the air cleaner is collected can be predicted through output values of the deep learning model. Here, “indoor dust concentration progress” can refer to indoor air quality changes over time. More specifically, it can refer to a tendency with respect to whether indoor air quality decreases or increases.

The processor can predict the tendency by applying the dust concentration data collected from the air cleaner to an artificial intelligence learning model. The artificial intelligence learning model used to predict the tendency is not limited to the aforementioned deep learning model and the above-described deep learning algorithms such as MLP, CNN and RNN can be applied in various manners.

The processor can receive outside dust concentration data from the KMA server (S820).

The processor can determine whether ventilation is required on the basis of a result value of prediction of indoor dust concentration progress and outside dust concentration data (S830).

For example, the processor can determine that ventilation is not required when it is determined that the predicted indoor dust concentration progress is a pattern of gradual improvement. Further, when the predicted indoor dust concentration progress is determined to be a tendency to degrade, the processor can compare the indoor dust concentration progress with KMA server data. When an indoor dust concentration is higher than an outside dust concentration, further operation of the air cleaner is ended and it can be determined that ventilation is required. However, the indoor dust concentration is lower than the outside dust concentration, it can be determined that ventilation is not required.

The processor can transmit a ventilation alarm message to the air cleaner or the user terminal when there is a necessity for ventilation according to a result of determination of whether ventilation is required (S840).

Hereinafter, an operation of determining necessity for ventilation in the cloud server 20 will be described in more detail with reference to FIG. 9.

FIG. 9 is a diagram for describing a detailed operation of the cloud server for performing the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention. In FIG. 9, operation of a 5G network is described as operation of the cloud server, more specifically, the processor of the cloud server.

Referring to FIG. 9, the processor can perform a process of preparing data in order to use indoor dust concentration data received from the air cleaner 10 for AI processing (S900).

As described above, when dust concentrations measured by a sensor of the air cleaner 10 are equal to or greater than a predetermined reference value N time or more continuously, the cloud server 20 receives the data. However, the present invention is not limited thereto and intervals at which dust concentration data is received from the air cleaner may be varied in various manners. Accordingly, it is possible to determine whether dust data concentrations exceed the reference value N times continuously through AI processing (S910).

The processor can apply N pieces of dust data as input values of a DNN model (S920) and predict indoor dust concentration progress through outputs of the DNN model (S930).

If dust concentrations sensed N times continuously for a predetermined time are less than the reference value, the processor can continuously prepare indoor dust concentration data received from the air cleaner without performing AI processing (S910: N).

When a predicted pattern is determined to be a dust concentration increasing pattern (S940): Y), the processor can control interoperation with the KMA system (S950). For example, the processor can request outside dust concentration data from the KMA server. The processor can receive an outside dust concentration from the KMA server in response to the request (S960).

The processor determines that ventilation is required (S980) when an average value of DNN input data is greater than an outside dust concentration (S970).

Hereinafter, standards for learning a deep learning model having dust concentration data received from the air cleaner 10 as input values will be described.

FIG. 10 is a diagram for describing deep learning model learning standards according to an embodiment of the present invention.

Referring to FIG. 10, the air cleaner 10 can collect a large amount of indoor dust concentration data for twenty-four hours, for example. When pattern analysis is required for certain dust concentration data, a pattern in which collected dust concentrations exceed a predetermined reference value needs to be analyzed.

In the graph shown in FIG. 10, section A (1 hour) shows an example in which measured dust concentrations are higher than a reference value in the entire section for 1 hour, and section B (1 hour) shows a pattern in which measured data concentrations are higher than the reference value only for first 30 minutes and lower than the reference value for last 30 minutes. According to a conventional statistical approach for dust concentration data, dust concentration data needs to be collected for one hour and observed in order to predict dust concentration progress in each of section A and section B.

However, when embodiments of the present invention are applied, the deep learning model is learned such that a tendency of dust concentration data for last 30 minutes is predicted on the basis of a pattern of dust concentration data for first 30 minutes in section A. That is, the deep learning model can be learned by setting data for first 30 minutes of section A to input values of the deep learning model and supervising data for last 30 minutes as output values of the deep learning model. Likewise, the deep learning model can be learned by setting data for first 30 minutes of section B to input values of the deep learning model and supervising data for last 30 minutes as output values of the deep learning model.

According to the learned deep learning model, it is possible to predict that indoor dust concentration progress measured for last 30 minutes will be continuously higher than the reference value according to dust concentration data collected for first 30 minutes in the case of section A. Likewise, according to the learned deep learning model, it is possible to predict that indoor dust concentration progress measured for last 30 minutes will be continuously lower than the reference value according to dust concentration data collected for first 30 minutes in the case of section B.

Accordingly, when the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention is applied, 50% or more of dust concentration data which is an object of collection and analysis for ventilation recommendation can be reduced. Furthermore, it is possible to secure accuracy equal to or higher than that of dust concentration progress prediction according to a conventional statistical approach while reducing the amount of data required.

FIG. 11 is a diagram for describing various examples of indoor air quality patterns used for deep learning model learning through data clustering according to an embodiment of the present invention. FIG. 12 is a diagram for describing a deep learning model learning patterns through input factors and result factors according to an embodiment of the present invention.

Referring to FIG. 11, there may be an air quality pattern (ventilation non-recommendation) in which fine dust concentrations remain in a high state for a certain time and then become lower. Further, there may be patterns (recommendation of ventilation) in which fine dust concentrations are irregular for time periods or continuously remain in a high state, or a high state and a low state are repeated for a short time. According to an embodiment of the present invention, an AI processor of the cloud server can classify air quality patterns shown in FIG. 11 into a ventilation non-recommendation pattern and a ventilation recommendation pattern.

Referring to FIG. 12, after the aforementioned classification standard is defined, a deep learning model can be learned such that an output of the deep learning model has a “ventilation recommendation” or “ventilation non-recommendation” value for each of a plurality of input factors.

FIG. 12 is a diagram referred to for description of deep learning.

Deep learning, a kind of machine learning, is multi-level learning from a deep level on the basis of data. Deep learning can indicate a set of machine learning algorithms that use multiple layers to extract essential data from a plurality of pieces of data.

A deep learning architecture can include an artificial neural network (ANN) and can be composed of, for example, deep neural network (DNN) such as a convolutional neural network (CNN), a recurrent neural network (RNN) or a deep belief network (DBN).

Referring to FIG. 12, the ANN can include an input layer, a hidden layer and an output layer. Each layer includes a plurality of nodes and is connected to the next layer. Nodes between neighboring layers can be connected to each other having weights.

A computer (machine) discovers a specific pattern from input data applied thereto to form a feature map. The computer (machine) can extract low-level features, intermediate-level features and high-level features to recognize an object and output a recognition result.

The ANN can abstract the next layer as features of a higher level.

Referring to FIG. 12, each node can operate on the basis of an activation model, and an output value corresponding to an input value can be determined according to the activation model.

An arbitrary node, for example, an output value of a low-level feature, can be input to the next layer connected to the corresponding node, for example, a node of an intermediate-level feature. A node of the next layer, for example, a node of an intermediate-level feature, can receive values output from a plurality of nodes of low-level features.

Here, an input value of each node may be a value obtained by applying a weight to an output value of a node of a previous layer. A weight can refer to strength of connection between nodes.

Further, deep learning may be regarded as a process of discovering an appropriate weight.

Further, an arbitrary node, for example, an output value of an intermediate-level feature, can be input to the next layer connected to the corresponding node, for example, a node of a high-level feature. A node of the next layer, for example, a node of a high-level feature, can receive values output from a plurality of nodes of intermediate-level features.

An ANN can extract feature information corresponding to each level using a learned layer corresponding to each level. The ANN can perform sequential abstraction to recognize a predetermined object using feature information of the highest level.

For example, in a face recognition process using deep learning, a computer can distinguish bright pixels and dark pixels from an input image according to pixel brightness, detect simple forms such as outlines and edges and then detect more complicated forms and objects. Finally, the computer can detect a form that defines a human face.

A deep learning architecture according to the present invention can use various known architectures. For example, the deep learning architecture according to the present invention may be a convolutional neural network (CNN), a recurrent neural network (RNN), a deep belief network (DBN) or the like.

The RNN is mainly used for natural language processing, is an architecture effective for processing time-series data varying over time, and can accumulate layers every moment to constitute an artificial neural network architecture. The DBN is a deep learning architecture configured by accumulating a restricted Boltzman machine (RBM) that is a deep learning technique in multiple layers. When RBM learning is repeated to achieve a predetermined number of layers, a DNB having the corresponding number of layers can be formed. The CNN is a model simulating the human brain function, which is generated on the assumption that a person extracts basic features of an object and then recognizes the object on the basis of results of complicated calculations in the brain when the person recognizes the object.

Meanwhile, learning of an artificial neural network can be performed by adjusting a weight of a connection line between nodes such that a desired output is obtained for a given input (adjusting a bias if required). Further, an artificial neural network can continuously update weight values through learning. In addition, a method such as back propagation may be used for learning of an artificial neural network.

Furthermore, the AI apparatus may store weights and biases constituting the DNN architecture according to an embodiment. Further, weights and biases constituting the DNN may be stored in an embedded memory of a pattern recognition module.

Referring to FIG. 12, the AI processor in the cloud server can update weight values applied to the deep learning model such that a specific output (ventilation recommendation or ventilation non-recommendation) is output on the basis of input factors shown in FIG. 12.

Further, referring to FIG. 12, when input factors (e.g., D1 to D6) are input to a trained deep learning model, a ventilation recommendation or ventilation non-recommendation result value may be derived.

As an example of an input factor, D1 may be an average value of dust concentration data for 1 to 5 minutes. D2 may be an average value of dust concentration data for 6 to 10 minutes on the basis of PM 2.5. D3 to D6 can be defined in the same manner. Although the aforementioned input factors have been described as examples, similar result values can be output within a similar reliability range through dust concentration pattern prediction of a learned deep learning model even though any input factor is applied to the learned deep learning model.

FIG. 13 is a diagram for describing an example of determining necessity for ventilation using a learned deep learning model according to an embodiment of the present invention.

Referring to FIG. 13, the AI processor of the cloud server 20 can set indoor dust concentration data received from the air cleaner 10 as input values of a deep learning model. The input values may be PM 2.5 average values every five minutes and may have different dust concentration average values.

Referring to FIGS. 12 and 13, the AI processor of the cloud server 20 can assign a first weight Weight_1 to data input to an input layer, add a first bias bias_1 thereto and transfer the resultant value to a first hidden layer (S1310).

The AI processor can assign a second weight Weight_2 to data input to the first hidden layer, add a second bias bias_2 thereto and transfer the resultant value to a second hidden layer (S1320). Learning operations (S1330, S1340 and S1350) in the same pattern can be performed on the remaining third and fourth hidden layers. A series of processes of outputting a value from an input layer to an output layer through hidden layers in the deep learning model can be realized by a separate processor or a separate program which controls the deep learning model.

The number of hidden layers and the number of outputs of each hidden layer may depend on a deep learning network architecture. According to an embodiment of the present invention, if the first hidden layer is defined as 20 unit, the second hidden layer is defined as 16 unit, the third hidden layer is defined as 10 unit, and the fourth hidden layer is defined as 4 unit, the last output layer is 1 unit and can be configured to have an output value of “1” when ventilation is required and an output value of “0” when ventilation is not required.

FIGS. 14 and 15 are diagrams for describing results of tests to which the indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention is applied.

FIG. 14 illustrates results of a first test of recommending ventilation for 50 minutes on the basis of data collected by the air cleaner for 30 minutes. As a result of the first test, when the test is performed after a DNN model is trained such that ventilation recommendation is performed 2979 times and ventilation non-recommendation is performed 2089 times, accuracy of 78.4% can be achieved. As a result of a second test of recommending ventilation for 60 minutes on the basis of data collected for 30 minutes, a DNN model with accuracy of 71.3% can be implemented.

The above-described present invention can be implemented with computer-readable code in a computer-readable medium in which program has been recorded. The computer-readable medium may include all kinds of recording devices capable of storing data readable by a computer system. Examples of the computer-readable medium may include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, magnetic tapes, floppy disks, optical data storage devices, and the like and also include such a carrier-wave type implementation (for example, transmission over the Internet). Therefore, the above embodiments are to be construed in all aspects as illustrative and not restrictive. The scope of the invention should be determined by the appended claims and their legal equivalents, not by the above description, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein. 

1. An indoor air quality control method using an intelligent air cleaner, comprising: receiving, from the air cleaner, dust concentration data of an indoor place where the air cleaner is located; predicting indoor dust concentration progress on the basis of output values of a learning model having the received dust concentration data as input values; receiving outside dust concentration data from an external server; determining whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data; and controlling an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed at predetermined intervals.
 2. The indoor air quality control method of claim 1, wherein the receiving of the indoor dust concentration data from the air cleaner comprises receiving a certain percentage or higher of data in a bad state on the basis of PM 2.5 forecast from among data sensed by the air cleaner at predetermined intervals.
 3. The indoor air quality control method of claim 1, wherein the predicting of the indoor dust concentration progress comprises: determining whether dust concentration data continuously sensed N times exceeds the predetermined reference value; and defining data of N dust concentrations as input values of a deep learning model and predicting the indoor dust concentration progress through output values of the deep learning model.
 4. The indoor air quality control method of claim 3, further comprising, when progress of additional N dust concentrations sensed after the N dust concentrations are sensed is predicted to increase as a result of prediction of the indoor dust concentration progress: requesting the outside dust concentration data from the Meteorological Administration server; and determining that ventilation is required when an average value of input data of the deep learning model is greater than the outside dust concentration data received from the Meteorological Administration server.
 5. The indoor air quality control method of claim 1, wherein the indoor dust concentration progress is predicted as one of a pattern in which the same number of dust concentrations as the number of pieces of dust concentration data received from the intelligent air cleaner increase, a pattern in which the dust concentrations decrease and a pattern in which the dust concentrations remains in a current state.
 6. The indoor air quality control method of claim 5, wherein the determining of whether ventilation is required comprises determining that ventilation is required when the indoor dust concentration progress is determined to be the increasing pattern or the current state remaining pattern.
 7. The indoor air quality control method of claim 5, wherein the determining of whether ventilation is required comprises: determining that ventilation is not required when the indoor dust concentration progress is predicted as the increasing pattern and an outside dust concentration received from the external server is higher than the indoor dust concentration; and controlling the air cleaner to continuously operate.
 8. The indoor air quality control method of claim 5, wherein the determining of whether ventilation is required further comprises predicting a ventilation time, wherein it is determined that ventilation is required when the indoor dust concentration progress is predicted as the decreasing pattern and it is predicted that the outside dust concentration is lower than the indoor dust concentration at a specific time of the decreasing pattern.
 9. The indoor air quality control method of claim 1, wherein the controlling of output of the alarm comprises controlling an indoor dust concentration state and whether ventilation is required to be output through audio.
 10. The indoor air quality control method of claim 1, wherein the receiving of the indoor dust concentration data further comprises receiving, in a state in which the air cleaner is powered off, the indoor dust concentration data sensed through a sensing unit in a state in which the air cleaner is in a standby state, further comprising controlling the air controller to be activated when it is determined that ventilation is required.
 11. The indoor air quality control method of claim 1, wherein the determining of whether ventilation is required comprises adaptively controlling a ventilation recommendation standard in consideration of characteristics of an occupant residing in the indoor place.
 12. An indoor air quality control apparatus using an intelligent air cleaner, comprising: a RF communication module; a memory storing a deep learning model; and a processor configured to determine whether to perform ventilation in a space in which the air cleaner is located on the basis of indoor dust concentration data received from the air cleaner and outside dust concentration data received from the Meteorological Administration server, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed at predetermined intervals, and wherein the processor predicts indoor dust concentration progress on the basis of output values of the deep learning model having the received dust concentration data as input values and controls an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required.
 13. The indoor air quality control apparatus of claim 12, wherein the processor determines whether dust concentration data continuously sensed N times exceeds the predetermined reference value, defines data of N dust concentrations as input values of the deep learning model and predicts the indoor dust concentration progress through output values of the deep learning model.
 14. The indoor air quality control apparatus of claim 12, wherein, when progress of additional N dust concentrations sensed after the N dust concentrations are sensed is predicted to increase as a result of prediction of the indoor dust concentration progress, the processor requests the outside dust concentration data from the Meteorological Administration server and determines that ventilation is required when an average value of input data of the deep learning model is greater than the outside dust concentration data received from the Meteorological Administration server.
 15. An indoor air quality control system, comprising: an intelligence air cleaner for acquiring indoor dust concentration data; and a cloud server for receiving the indoor dust concentration data from the air cleaner, wherein the received dust concentration data is data exceeding a predetermined reference value among data sensed by the air cleaner at predetermined intervals, and wherein the cloud server predicts indoor dust concentration progress on the basis of output values of a deep learning model having the received dust concentration data as input values, receives outside dust concentration data from an external server, determines whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data, and outputs an alarm to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required.
 16. A non-transitory computer-readable medium storing a computer-executable component configured to be executed in one or more processors of a computer device, the computer-executable component being configured: to receive, from an air cleaner, dust concentration data of an indoor place where the air cleaner is located; to predict indoor dust concentration progress on the basis of output values of a learning model having the received dust concentration data as input values; to receive outside dust concentration data from an external server; to determine whether ventilation is required by comparing the predicted indoor dust concentration progress with the outside dust concentration data; and to control an alarm to be output to the air cleaner or a mobile terminal associated with the air cleaner according to whether ventilation is required, wherein the received dust concentration data includes data exceeding a predetermined reference value among data sensed at predetermined intervals. 